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Everything about Neuroevolution

Form of artificial intelligence

Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANNs), parameters, and rules. It is most commonly applied in artificial life, general game playing and evolutionary robotics. The main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use backpropagation with a fixed topology. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: NeuroevolutionA 5-minute orientation
  2. Skim a recent paper: Accurate prediction of heat conductivity of water by a neuroevolution potentialInfluential work since 2022
  3. Pick up a classic book: Handbook of Neuroevolution Through ErlangThe most-reprinted book on the subject
  4. Read the landmark paper: Designing neural networks through neuroevolutionCited 597 times
  5. Go sideways: Neuroevolution of augmenting topologiesA closely related topic
  6. Go sideways: Evolutionary acquisition of neural topologiesA closely related topic

📖 Definitions

Noun The class of methods for training artificial neural networks with genetic algorithms.

🗂️ The subject at a glance

  1. Features
  2. Comparison with gradient descent
  3. Direct and indirect encoding
  4. Examples

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics